Biostat 203B Homework 2

Due Feb 8, 2026 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-unknown-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/aarch64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/aarch64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.2    fastmap_1.2.0     cli_3.6.3        
 [5] tools_4.4.2       htmltools_0.5.8.1 rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_1.8.9    xfun_0.49        
[13] digest_0.6.37     rlang_1.1.4       evaluate_1.0.1   

Load necessary libraries (you can add more as needed).

library(arrow)

Some features are not enabled in this build of Arrow. Run `arrow_info()` for more information.

Attaching package: 'arrow'

The following object is masked from 'package:utils':

    timestamp

library(data.table)
library(duckdb)

Loading required package: DBI

library(memuse)
library(pryr)

Attaching package: 'pryr'

The following object is masked from 'package:data.table':

    address

library(R.utils)

Loading required package: R.oo

Loading required package: R.methodsS3

R.methodsS3 v1.8.2 (2022-06-13 22:00:14 UTC) successfully loaded. See ?R.methodsS3 for help.

R.oo v1.27.0 (2024-11-01 18:00:02 UTC) successfully loaded. See ?R.oo for help.

Attaching package: 'R.oo'

The following object is masked from 'package:R.methodsS3':

    throw

The following objects are masked from 'package:methods':

    getClasses, getMethods

The following objects are masked from 'package:base':

    attach, detach, load, save

R.utils v2.12.3 (2023-11-18 01:00:02 UTC) successfully loaded. See ?R.utils for help.

Attaching package: 'R.utils'

The following object is masked from 'package:arrow':

    timestamp

The following object is masked from 'package:utils':

    timestamp

The following objects are masked from 'package:base':

    cat, commandArgs, getOption, isOpen, nullfile, parse, use, warnings

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()      masks data.table::between()
✖ purrr::compose()      masks pryr::compose()
✖ lubridate::duration() masks arrow::duration()
✖ tidyr::extract()      masks R.utils::extract()
✖ dplyr::filter()       masks stats::filter()
✖ dplyr::first()        masks data.table::first()
✖ lubridate::hour()     masks data.table::hour()
✖ lubridate::isoweek()  masks data.table::isoweek()
✖ dplyr::lag()          masks stats::lag()
✖ dplyr::last()         masks data.table::last()
✖ lubridate::mday()     masks data.table::mday()
✖ lubridate::minute()   masks data.table::minute()
✖ lubridate::month()    masks data.table::month()
✖ purrr::partial()      masks pryr::partial()
✖ lubridate::quarter()  masks data.table::quarter()
✖ lubridate::second()   masks data.table::second()
✖ purrr::transpose()    masks data.table::transpose()
✖ lubridate::wday()     masks data.table::wday()
✖ lubridate::week()     masks data.table::week()
✖ dplyr::where()        masks pryr::where()
✖ lubridate::yday()     masks data.table::yday()
✖ lubridate::year()     masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Display memory information of your computer

memuse::Sys.meminfo()

Totalram:  15.598 GiB 
Freeram:   14.253 GiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/

total 6153128
-rw-r--r-- 1 rstudio rstudio   19928140 Jun 24  2024 admissions.csv.gz
-rw-r--r-- 1 rstudio rstudio     427554 Apr 13  2024 d_hcpcs.csv.gz
-rw-r--r-- 1 rstudio rstudio   33564802 Oct  3  2024 diagnoses_icd.csv.gz
-rw-r--r-- 1 rstudio rstudio     876360 Apr 13  2024 d_icd_diagnoses.csv.gz
-rw-r--r-- 1 rstudio rstudio     589186 Apr 13  2024 d_icd_procedures.csv.gz
-rw-r--r-- 1 rstudio rstudio      13169 Oct  3  2024 d_labitems.csv.gz
-rw-r--r-- 1 rstudio rstudio    9743908 Oct  3  2024 drgcodes.csv.gz
-rw-r--r-- 1 rstudio rstudio  811305629 Apr 13  2024 emar.csv.gz
-rw-r--r-- 1 rstudio rstudio  748158322 Apr 13  2024 emar_detail.csv.gz
-rw-r--r-- 1 rstudio rstudio    2162335 Apr 13  2024 hcpcsevents.csv.gz
-rw-r--r-- 1 rstudio rstudio       2907 Dec 29  2024 index.html
-rw-r--r-- 1 rstudio rstudio 2592909134 Feb 20  2025 labevents.csv.gz
-rw-r--r-- 1 rstudio rstudio  117644075 Oct  3  2024 microbiologyevents.csv.gz
-rw-r--r-- 1 rstudio rstudio   44069351 Oct  3  2024 omr.csv.gz
-rw-r--r-- 1 rstudio rstudio    2835586 Apr 13  2024 patients.csv.gz
-rw-r--r-- 1 rstudio rstudio  525708076 Apr 13  2024 pharmacy.csv.gz
-rw-r--r-- 1 rstudio rstudio  666594177 Apr 13  2024 poe.csv.gz
-rw-r--r-- 1 rstudio rstudio   55267894 Apr 13  2024 poe_detail.csv.gz
-rw-r--r-- 1 rstudio rstudio  606298611 Apr 13  2024 prescriptions.csv.gz
-rw-r--r-- 1 rstudio rstudio    7777324 Apr 13  2024 procedures_icd.csv.gz
-rw-r--r-- 1 rstudio rstudio     127330 Apr 13  2024 provider.csv.gz
-rw-r--r-- 1 rstudio rstudio    8569241 Apr 13  2024 services.csv.gz
-rw-r--r-- 1 rstudio rstudio   46185771 Oct  3  2024 transfers.csv.gz

ls -l ~/mimic/icu/

total 4267740
-rw-r--r-- 1 rstudio rstudio      41566 Apr 13  2024 caregiver.csv.gz
-rw-r--r-- 1 rstudio rstudio 3502392765 Apr 13  2024 chartevents.csv.gz
-rw-r--r-- 1 rstudio rstudio   63481196 Apr 13  2024 datetimeevents.csv.gz
-rw-r--r-- 1 rstudio rstudio      58741 Apr 13  2024 d_items.csv.gz
-rw------- 1 rstudio rstudio   14686092 Feb 22  2025 icustays.csv
-rw-r--r-- 1 rstudio rstudio    3342355 Oct  3  2024 icustays.csv.gz
-rw-r--r-- 1 rstudio rstudio       1336 Dec 29  2024 index.html
-rw-r--r-- 1 rstudio rstudio  311642048 Apr 13  2024 ingredientevents.csv.gz
-rw-r--r-- 1 rstudio rstudio  401088206 Apr 13  2024 inputevents.csv.gz
-rw-r--r-- 1 rstudio rstudio   49307639 Apr 13  2024 outputevents.csv.gz
-rw-r--r-- 1 rstudio rstudio   24096834 Apr 13  2024 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage; all these readers can take gz file as input without explicit decompression.)

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv.)

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 130x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

-rw-r--r-- 1 rstudio rstudio 2592909134 Feb 20  2025 /home/rstudio/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

labevent_id,subject_id,hadm_id,specimen_id,itemid,order_provider_id,charttime,storetime,value,valuenum,valueuom,ref_range_lower,ref_range_upper,flag,priority,comments
1,10000032,,2704548,50931,P69FQC,2180-03-23 11:51:00,2180-03-23 15:56:00,___,95,mg/dL,70,100,,ROUTINE,"IF FASTING, 70-100 NORMAL, >125 PROVISIONAL DIABETES."
2,10000032,,36092842,51071,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
3,10000032,,36092842,51074,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
4,10000032,,36092842,51075,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"BENZODIAZEPINE IMMUNOASSAY SCREEN DOES NOT DETECT SOME DRUGS,;INCLUDING LORAZEPAM, CLONAZEPAM, AND FLUNITRAZEPAM."
5,10000032,,36092842,51079,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,
6,10000032,,36092842,51087,P69FQC,2180-03-23 11:51:00,,,,,,,,ROUTINE,RANDOM.
7,10000032,,36092842,51089,P69FQC,2180-03-23 11:51:00,2180-03-23 16:15:00,,,,,,,ROUTINE,PRESUMPTIVELY POSITIVE.
8,10000032,,36092842,51090,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,METHADONE ASSAY DETECTS ONLY METHADONE (NOT OTHER OPIATES/OPIOIDS).
9,10000032,,36092842,51092,P69FQC,2180-03-23 11:51:00,2180-03-23 16:00:00,NEG,,,,,,ROUTINE,"OPIATE IMMUNOASSAY SCREEN DOES NOT DETECT SYNTHETIC OPIOIDS;SUCH AS METHADONE, OXYCODONE, FENTANYL, BUPRENORPHINE, TRAMADOL,;NALOXONE, MEPERIDINE.  SEE ONLINE LAB MANUAL FOR DETAILS."

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 3 minutes on your computer, then abort the program and report your findings.

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

Q2.3 Ingest a subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: albumin (50862), creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: Use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. Do not put labevents_filtered.csv.gz in Git! To save render time, you can put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true before rendering your qmd file.)

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file, excluding the header? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory (do not add it in git!). To save render time, put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true when rendering your qmd file.

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is Apache Arrow. Imagine you want to explain it to a layman in an elevator.

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is the Parquet format. Imagine you want to explain it to a layman in an elevator.

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what is DuckDB. Imagine you want to explain it to a layman in an elevator.

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226512,39.4,39.4,kg,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226707,60,60,Inch,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226730,152,152,cm,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,220048,SR (Sinus Rhythm),,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224642,Oral,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224650,None,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:20:00,223761,98.7,98.7,°F,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220179,84,84,mmHg,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220180,48,48,mmHg,0

How many rows? 433 millions.

zcat < ~/mimic/icu/chartevents.csv.gz | tail -n +2 | wc -l

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.

Q4. AI assistant

Which AI assistants (e.g., GitHub Copilot) do you use when working on this assignment? Which AI model (e.g., GPT-5 mini, GPT-5, Claude Sonnet 4.5) does the AI assistant use? How do you use them? Do you think they help improve your productivity?

Give 5 instances where AI model gave incorrect or misleading answers. You can use screenshots or copy-paste the Q&A.